Platform independent profiling of a QCD code

The supercomputing platforms available for high performance computing based research evolve at a great rate. However, this rapid development of novel technologies requires constant adaptations and optimizations of the existing codes for each new machine architecture. In such context, minimizing time of efficiently porting the code on a new platform is of crucial importance. A possible solution for this common challenge is to use simulations of the application that can assist in detecting performance bottlenecks. Due to prohibitive costs of classical cycle-accurate simulators, coarse-grain simulations are more suitable for large parallel and distributed systems. We present a procedure of implementing the profiling for openQCD code [1] through simulation, which will enable the global reduction of the cost of profiling and optimizing this code commonly used in the lattice QCD community. Our approach is based on well-known SimGrid simulator [2], which allows for fast and accurate performance predictions of HPC codes. Additionally, accurate estimations of the program behavior on some future machines, not yet accessible to us, are anticipated

Versatile, Scalable, and Accurate Simulation of Distributed Applications and Platforms

International audienceThe study of parallel and distributed applications and platforms, whether in the cluster, grid, peer-to-peer, volunteer, or cloud computing domain, often mandates empirical evaluation of proposed algorithmic and system solutions via simulation. Unlike direct experimentation via an application deployment on a real-world testbed, simulation enables fully repeatable and configurable experiments for arbitrary hypothetical scenarios. Two key concerns are accuracy (so that simulation results are scientifically sound) and scalability (so that simulation experiments can be fast and memory-efficient). While the scalability of a simulator is easily measured, the accuracy of many state-of-the-art simulators is largely unknown because they have not been sufficiently validated. In this work we describe recent accuracy and scalability advances made in the context of the SimGrid simulation framework. A design goal of SimGrid is that it should be versatile, i.e., applicable across all aforementioned domains. We present quantitative results that show that SimGrid compares favorably to state-of-the-art domain-specific simulators in terms of scalability, accuracy, or the trade-off between the two. An important implication is that, contrary to popular wisdom, striving for versatility in a simulator is not an impediment but instead is conducive to improving both accuracy and scalability

A complete simulator for volunteer computing environments

Volunteer computing is a type of distributed computing in which ordinary people donate their idle computer time to science projects like SETI@home, Climateprediction.net and many others. BOINC provides a complete middleware system for volunteer computing, and it became generalized as a platform for distributed applications in areas as diverse as mathematics, medicine, molecular biology, climatology, environmental science, and astrophysics. In this document we present the whole development process of ComBoS, a complete simulator of the BOINC infrastructure. Although there are other BOINC simulators, our intention was to create a complete simulator that, unlike the existing ones, could simulate realistic scenarios taking into account the whole BOINC infrastructure, that other simulators do not consider: projects, servers, network, redundant computing, scheduling, and volunteer nodes. The output of the simulations allows us to analyze a wide range of statistical results, such as the throughput of each project, the number of jobs executed by the clients, the total credit granted and the average occupation of the BOINC servers. This bachelor thesis describes the design of ComBoS and the results of the validation performed. This validation compares the results obtained in ComBoS with the real ones of three different BOINC projects (Einstein@home, SETI@home and LHC@home). Besides, we analyze the performance of the simulator in terms of memory usage and execution time. This document also shows that our simulator can guide the design of BOINC projects, describing some case studies using ComBoS that could help designers verify the feasibility of BOINC projects.Ingeniería Informátic

ComBos: a complete simulator of volunteer computing and desktop grids

Volunteer Computing is a type of distributed computing in which ordinary people donate their idle computer time to science projects like SETI@Home, Climateprediction.net and many others. In a similar way, Desktop Grid Computing is a form of distributed computing in which an organization uses its existing computers to handle its own long-running computational tasks. BOINC is the main middleware that provides a software platform for Volunteer Computing and desktop grid computing, and it became generalized as a platform for distributed applications in areas as diverse as mathematics, medicine, molecular biology, climatology, environmental science, and astrophysics. In this paper we present a complete simulator of BOINC infrastructures, called ComBoS. Although there are other BOINC simulators, none of them allow us to simulate the complete infrastructure of BOINC. Our goal was to create a complete simulator that, unlike the existing ones, could simulate realistic scenarios taking into account the whole BOINC infrastructure, that other simulators do not consider: projects, servers, network, redundant computing, scheduling, and volunteer nodes. The outputs of the simulations allow us to analyze a wide range of statistical results, such as the throughput of each project, the number of jobs executed by the clients, the total credit granted and the average occupation of the BOINC servers. The paper describes the design of ComBoS and the results of the validation performed. This validation compares the results obtained in ComBoS with the real ones of three different BOINC projects (Einstein@Home, SETI@Home and LHC@Home). Besides, we analyze the performance of the simulator in terms of memory usage and execution time. The paper also shows that our simulator can guide the design of BOINC projects, describing some case studies using ComBoS that could help designers verify the feasibility of BOINC projects. (C) 2017 Elsevier B.V. All rights reserved.This work has been partially supported by the Spanish MINISTERIO DE ECONOMÍA Y COMPETITIVIDAD under the project grant TIN2016-79637-P TOWARDS UNIFICATION OF HPC AND BIG DATA PARADIGMS

A Toolkit for Simulation of Desktop Grid Environment

Peer to Peers, clusters and grids enable a combination of heterogeneous distributed recourses to resolve problems in different fields such as science, engineering and commerce. Organizations within the world wide grid environment network are offering geographically distributed resources which are administrated by schedulers and policies. Studying the resources behavior is time consuming due to their unique behavior and uniqueness. In this type of environment it is nearly impossible to prove the effectiveness of a scheduling algorithm. Hence the main objective of this study is to develop a desktop grid simulator toolkit for measuring and modeling scheduler algorithm performance. The selected methodology for the application development is based on prototyping methodology. The prototypes will be developed using JAVA language united with a MySQL database. Core functionality of the simulator are job generation, volunteer generation, simulating algorithms, generating graphical charts and generating reports. A simulator for desktop grid environment has been developed using Java as the implementation language due to its wide popularity. The final system has been developed after a successful delivery of two prototypes. Despite the implementation of the mentioned core functionalities of a desktop grid simulator, advanced features such as viewing real-time graphical charts, generating PDF reports of the simulation result and exporting the final result as CSV files has been also included among the other features

Towards Scalable, Accurate, and Usable Simulations of Distributed Applications and Systems

The study of parallel and distributed applications and platforms, whether in the cluster, grid, peer-to-peer, volunteer, or cloud computing domain, often mandates empirical evaluation of proposed algorithm and system solutions via simulation. Unlike direct experimentation via an application deployment on a real-world testbed, simulation enables fully repeatable and configurable experiments that can often be conducted quickly for arbitrary hypothetical scenarios. In spite of these promises, current simulation practice is often not conducive to obtaining scientifically sound results. State-of-the-art simulators are often not validated and their accuracy is unknown. Furthermore, due to the lack of accepted simulation frameworks and of transparent simulation methodologies, published simulation results are rarely reproducible. We highlight recent advances made in the context of the SimGrid simulation framework in a view to addressing this predicament across the aforementioned domains. These advances, which pertain both to science and engineering, together lead to unprecedented combinations of simulation accuracy and scalability, allowing the user to trade off one for the other. They also enhance simulation usability and reusability so as to promote an Open Science approach for simulation-based research in the field.L'étude de systèmes et applications parallèles et distribués, qu'il s'agisse de clusters, de grilles, de systèmes pair-à-pair de volunteer computing, ou de cloud, demandent souvent l'évaluation empirique par simulation des algorithmes et solutions proposés. Contrairement à l'expérimentation directe par déploiement d'applications sur des plates-formes réelles, la simulation permet des expériences reproductibles pouvant être menée rapidement sur n'importe quel scénario hypothétique. Malgré ces avantages théoriques, les pratiques actuelles en matière de simulation ne permettent souvent pas d'obtenir des résultats scientifiquement éprouvés. Les simulateurs classiques sont trop souvent validés et leur réalisme n'est pas démontré. De plus, le manque d'environnements de simulation communément acceptés et de méthodologies classiques de simulation font que les résultats publiés grâce à cette approche sont rarement reproductibles par la communauté. Nous présentons dans cet article les avancées récentes dans le contexte de l'environnement SimGrid pour répondre à ces difficultés. Ces avancées, comprenant à la fois des aspects techniques et scientifiques, rendent possible une combinaison inégalée de réalisme et précision de simulation et d'extensibilité. Cela permet aux utilisateurs de choisir le grain des modèles utilisés pour ses simulations en fonction de ses besoins de réalisme et d'extensibilité. Les travaux présentés ici améliorent également l'utilisabilité et la réutilisabilité de façon à promouvoir l'approche d'Open Science pour les recherches basées sur la simulation dans notre domaine

Scheduling for Large Scale Distributed Computing Systems: Approaches and Performance Evaluation Issues

Although our everyday life and society now depends heavily oncommunication infrastructures and computation infrastructures,scientists and engineers have always been among the main consumers ofcomputing power. This document provides a coherent overview of theresearch I have conducted in the last 15 years and which targets themanagement and performance evaluation of large scale distributedcomputing infrastructures such as clusters, grids, desktop grids,volunteer computing platforms, ... when used for scientific computing.In the first part of this document, I present how I have addressedscheduling problems arising on distributed platforms (like computinggrids) with a particular emphasis on heterogeneity and multi-userissues, hence in connection with game theory. Most of these problemsare relaxed from a classical combinatorial optimization formulationinto a continuous form, which allows to easily account for keyplatform characteristics such as heterogeneity or complex topologywhile providing efficient practical and distributed solutions.The second part presents my main contributions to the SimGrid project,which is a simulation toolkit for building simulators of distributedapplications (originally designed for scheduling algorithm evaluationpurposes). It comprises a unified presentation of how the questions ofvalidation and scalability have been addressed in SimGrid as well asthoughts on specific challenges related to methodological aspects andto the application of SimGrid to the HPC context

New approaches to data access in large-scale distributed system

Mención Internacional en el título de doctorA great number of scientific projects need supercomputing resources, such as, for example, those carried out in physics, astrophysics, chemistry, pharmacology, etc. Most of them generate, as well, a great amount of data; for example, a some minutes long experiment in a particle accelerator generates several terabytes of data. In the last years, high-performance computing environments have evolved towards large-scale distributed systems such as Grids, Clouds, and Volunteer Computing environments. Managing a great volume of data in these environments means an added huge problem since the data have to travel from one site to another through the internet. In this work a novel generic I/O architecture for large-scale distributed systems used for high-performance and high-throughput computing will be proposed. This solution is based on applying parallel I/O techniques to remote data access. Novel replication and data search schemes will also be proposed; schemes that, combined with the above techniques, will allow to improve the performance of those applications that execute in these environments. In addition, it will be proposed to develop simulation tools that allow to test these and other ideas without needing to use real platforms due to their technical and logistic limitations. An initial prototype of this solution has been evaluated and the results show a noteworthy improvement regarding to data access compared to existing solutions.Un gran número de proyectos científicos necesitan recursos de supercomputación como, por ejemplo, los llevados a cabo en física, astrofísica, química, farmacología, etc. Muchos de ellos generan, además, una gran cantidad de datos; por ejemplo, un experimento de unos minutos de duración en un acelerador de partículas genera varios terabytes de datos. Los entornos de computación de altas prestaciones han evolucionado en los últimos años hacia sistemas distribuidos a gran escala tales como Grids, Clouds y entornos de computación voluntaria. En estos entornos gestionar un gran volumen de datos supone un problema añadido de importantes dimensiones ya que los datos tienen que viajar de un sitio a otro a través de internet. En este trabajo se propondrá una nueva arquitectura de E/S genérica para sistemas distribuidos a gran escala usados para cómputo de altas prestaciones y de alta productividad. Esta solución se basa en la aplicación de técnicas de E/S paralela al acceso remoto a los datos. Así mismo, se estudiarán y propondrán nuevos esquemas de replicación y búsqueda de datos que, en combinación con las técnicas anteriores, permitan mejorar las prestaciones de aquellas aplicaciones que ejecuten en este tipo de entornos. También se propone desarrollar herramientas de simulación que permitan probar estas y otras ideas sin necesidad de recurrir a una plataforma real debido a las limitaciones técnicas y logísticas que ello supone. Se ha evaluado un prototipo inicial de esta solución y los resultados muestran una mejora significativa en el acceso a los datos sobre las soluciones existentes.Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaPresidente: David Expósito Singh.- Secretario: María de los Santos Pérez Hernández.- Vocal: Juan Manuel Tirado Mart